Lipidome profiling of neutrophil-derived extracellular vesicles unveils their contribution to the ensemble of synovial fluid-derived extracellular vesicles during joint inflammation
Bibliographic record
Abstract
The molecular signature of cell-derived extracellular vesicles (EVs) from synovial fluid (SF) offers insights into the cells and molecular processes associated with joint disorders and can be exploited to define biomarkers. The EV-signature is determined by cargo molecules and the lesser-studied lipid bilayer. We here investigated the lipidome of SF-EVs in inflamed joints derived from Rheumatoid Arthritis (RA) and Spondyloarthritis (SpA) patients, two autoimmune-driven joint diseases, and compared these signatures to the lipid profile of equine SF-EVs obtained during induced acute synovitis. Since neutrophils are primary SF-infiltrating cells during these inflammatory joint diseases, we also analyzed how inflammatory stimuli alter the lipidomic profile of human and equine neutrophil-derived EVs (nEVs) in vitro and how these signatures relate to the lipidome signatures of SF-EVs from inflamed joints. We identified neutrophil stimulation intensity-dependent changes in the lipidomic profile of nEVs with elevated presence of dihexosylceramide (lactosylceramide), phosphatidylserine, and phosphatidylethanolamine ether-linked lipid classes in human nEVs upon full neutrophil activation. In horses, levels of monohexosylceramide (glucosylceramide) increased instead of dihexosylceramide, indicating species-specific differences. The lipid profiles of RA and SpA SF-EVs were relatively similar and showed a relative resemblance with stimulated human nEVs. Similarly, the lipidome of equine synovitis-derived SF-EVs closer resembled the one of stimulated equine nEVs. Hence, lipidome profiling can provide insights into the contribution of nEVs to the heterogeneous pool of SF-EVs, deepening our understanding of inflammatory joint diseases and revealing molecular changes in joint homeostasis, which can lead to the development of more precise disease diagnosis and treatment strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".